February 25, 2014. Working memory stems in part from genes encoding voltage-gated cation channels, according to a paper published online February 13 in Neuron. Led by Andreas Papassotiropoulos of the University of Basel, Switzerland, the study uses gene set analysis to find biological themes among common genetic variants associated with working memory. Voltage-gated cation channels turned up in multiple, independent samples, including one consisting of people with schizophrenia, which is marked by working memory impairment.

The new study complements genetic findings implicating voltage-gated calcium channel genes in schizophrenia (see SRF related conference report) and bipolar disorder (see SRF related news report). This suggests that the way neurons process information they receive is perturbed in psychiatric disorders—a different kind of pathology from the suspected faulty transfer of information between neurons based on links to genes for synaptic machinery (e.g., see SRF related news report).

The study also grapples with the problem of how to make sense of the rising number of genes associated with different conditions or traits as studies grow larger. Researchers have developed methods to group genes according to their functions and then ask whether a particular gene set is hit hard in a disorder. At the moment, however, a kind of Wild West of tools exists, making it difficult to gauge the replicability of any one result based on gene set analysis. The new study addresses this by putting its gene set analysis method to the test in multiple samples—from young, healthy people; elderly, non-demented people; people with schizophrenia; and in brain imaging data.

Gene-centered
First authors Angela Heck and Matthias Fastenrath began by looking for single nucleotide polymorphisms (SNPs) associated with working memory in a sample of 905 healthy, young adults. Their working memory was tested with the n-back task, in which participants are presented with a series of letters and asked to indicate whether the current letter matches the one shown two letters prior. This classic test of working memory involves remembering the sequence of letters and updating and manipulating that information.

A genomewide association study (GWAS) of performance on the n-back task turned up five SNPs that reached genomewide significance. But rather than focusing on the few genes with significant p values, the gene set analysis method, called MAGENTA, took a gene-centered approach by assigning for each gene the lowest p value obtained by any SNP within that gene in the GWAS. It then asked whether certain pre-defined gene sets were enriched for low p values compared to a random gene set.

The answer was yes for the voltage-gated cation channel activity gene set, as defined by the Gene Ontology database, which groups genes according to what is known about their function. This included multiple subunits of voltage-gated calcium channels such as CACNA1A, potassium channels, and sodium channels. This finding was replicated in an independent sample of 746 people tested, and analyzing the combined sample with two other gene set algorithms also found significant enrichment for the same gene set. Though working memory declines with age, significant enrichment in this gene set was detected in 763 elderly people evaluated with a different test of working memory, though this did not survive genomewide correction.

Because working memory is impaired in schizophrenia, the researchers tested whether this same gene set would be implicated by SNPs associated with risk for schizophrenia by GWAS. Using data from 32,143 individuals in the Psychiatric Genomics Consortium’s case-control samples (see SRF related news report), they again found enrichment for this voltage-gated cation channel activity gene set.

Overall, the results suggest that the ion channels controlling the excitability of a neuron contribute to the sustained neural activity thought to mediate working memory. To directly capture the influence of this particular gene set on brain activity, the researchers imaged 707 people from their young, healthy sample while they performed the n-back task. The researchers then calculated a gene set score for each individual based on their SNPs; this score reflected the genetic load of working memory-associated alleles within the voltage-gated ion channel gene set. Plotting this score against brain activity revealed positive correlations within the superior parietal cortex and the cerebellum—something that reinforces the idea that the cerebellum performs more than motor coordination (see SRF related news report). This relationship seemed specific to working memory, because no significant correlations emerged when brain activity during an episodic memory task was used instead.

This kind of analysis may reveal the contributions of other gene sets to other kinds of brain activity and provide a more deconstructed look at a heterogeneous disorder such as schizophrenia. Though the ion channel gene set found here suggests targets for drug development, manipulating such integral components of a neuron’s function may be tricky.—Michele Solis.

The article by Heck and colleagues provides additional support for the notion that common genetic factors influence risk for schizophrenia and working memory performance. While evidence that working memory performance is sensitive to genetic liability for schizophrenia was well established by twin and family studies (e.g., Cannon et al., 2000; Glahn et al., 2007), the current article extends these findings by suggesting that at least a portion of this joint effect is conferred by common variants in the voltage-gated cation channel activity (see QuickGO page) gene network. The paper provides a potential biological mechanism through which a set of genes could influence both traits. Such testable biological hypotheses are critical both for unraveling the genetic architecture of schizophrenia and other psychotic illnesses and for helping to characterize how these genes aid in manifesting the behavioral disorders. Thus, although the paper does not provide a single pleiotropic gene as would be required in classical human genetics, I believe the findings represent a true advancement in our understanding of schizophrenia genetics.

A major strength of the paper is the large number of independent samples with similar, but not identical, working memory measures that provided data for the discovery or replication samples. Papassotiropoulos and his group have applied this powerful approach to provide insight into the genetic make-up of cognitive processes, particularly memory.

Recently, there has been a good deal of debate concerning the utility of endophenotypes in the search for mental illness genes. As described by Gottesman and Gould (Gottesman and Gould, 2003), endophenotypes are measurable components unseen by the unaided eye along the pathway between disease and distal genotype. John Blangero and I pointed out that joint genetic determination (pleiotropy) of endophenotype and disease risk is fundamental to the endophenotype concept (Glahn et al., 2012). The current paper clearly demonstrates pleiotropy between schizophrenia risk and working memory performance, reinforcing working memory as a schizophrenia endophenotype and demonstrating how such traits can be used to provide testable biological hypotheses.

Finally, I would like to point out that this work was primarily conducted in healthy individuals not selected for mental illness. The endophenotype and normal variation strategy (e.g., using unselected samples to learn about the genetic factors influencing an endophenotype and then confirming these results in a sample selected for the illness) has worked in other areas of medicine, and I believe it will work in psychiatric genetics as well. Indeed, I think this paper demonstrated exactly that.

This paper by Ridler and colleagues is an interesting novel twist on other evidence that developmental milestones, even in traditionally hard wired motor functions, are slightly delayed in samples of individuals who later manifest schizophrenia. There are two important additional results that emerge from their analysis: first, that early developmental motor milestones predict MRI measures of brain volume and executive function during adulthood in normal individuals; and second, that these predictive relationships are not found in their schizophrenia sample.

The link between early motor and adult cognitive development is not surprising, as child development studies have highlighted the predictive implications of precocious motor development for cognitive development. The link to volumetric measures on MRI is intriguing and novel, though as in all MRI volumetry studies, the tissue compartments that contribute to variance in the MRI measures are uncertain. I doubt that MRI volume measures are sensitive assessments of variation in synaptic abundance or other elements related to synaptic plasticity. So, the biologic basis for this correlation is intriguing but uncertain.

Finally, the finding of no relationship between these measures in schizophrenia might reflect a deviation in development of the neural systems involved the patients, as the authors suggest, but it might also argue that there is a confounder in the schizophrenia results that obscures the relationships found in the normal subjects. One possibility is medication, which can affect cognitive measures and MRI measures.

The work by Ferreira et al. exemplifies the growing enthusiasm for collaborative work among investigators and marks the new era of collaborative genetic research in complex disorders. The LD data found in the extant HapMap SNPs allow investigators to use sophisticated computational approaches to impute genotypes based on these HapMap data sets and the data generated from the experimental sample, thereby maximizing the utility of the actual genotyping itself. Nothing short of brilliant. Correlates between imputed and true genotypes were estimated to be 0.987, which is quite good. The significance estimates of the combined data analyses of the three data sets identifies two genes (ANK3 and CACNA1C) in the genomewide significance range with a p value of 10-8, which is most reassuring and even more so considering that the CACNA1C gene was identified previously. The humbling fact in the mix is that the odds ratios are modest, ranging from 1.2 to 1.4, which is nonetheless in a similar arena as other complex genetic disorders such as diabetes. It is further humbling (and consistent with the modest ORs) to consider that the frequency of the risk allele for the CACNA1C gene is 7.5 percent in the BP cases and 5.6 percent in the unaffected control individuals. Finally, there was no effect of the sub-diagnostic categories, age of onset, presence of psychosis, or sex. The highly encouraging point is that these genes appear to be in pathways that are affected by lithium, the gold standard of care for BP disorder. The anchorage of a genetic finding within a mechanism of an established treatment for BP disorder (lithium) lends substantial credibility to overall results. The next questions of research will relate to the efficacy of lithium relative to genotypes of these genes and others within their pathways. These findings raise several clinical questions, and integration of clinical outcome patterns with genetic data can be expected to shed further light on the etiology of the disease and the genetics of treatment response. Long live lithium.

Ferreira et al. propose two specific genes to be related to bipolar disorder, ANK3, which is indirectly related to sodium channels, and CACNA1C, which is a calcium channel subunit. They hypothesize that bipolar disorder is, at least in part, a channelopathy. This hypothesis is consistent with a number of physiological observations made over the past several decades, as reviewed elsewhere.

The genetic data these authors present is certainly suggestive. They have analyzed three independent data sets, STEP-UCL (Sklar et al., 2008), Wellcome Trust (Wellcome Trust Case Control Consortium, 2007), and a third set called ED-DUB-STEP2 (not yet published). Their total sample exceeds 4,000 cases and 6,000 controls. They have direct genotype data on >300,000 SNPs and have imputed nearly 1.5 million additional. Their highest significance values (10-7 to 10-9) include a combination of genotyped and imputed SNPs. For each of these, the combined p value is a product of modest but consistent associations in the three independent data sets.

ANK3 features rs10994336 at 9x10e-9 and rs1938526 at 1x10e-8. By my reading, these two polymorphisms are both slightly distal to the gene but the second is within 10-20 kB. The first of these is imputed, and thus the p value should probably be judged as more imprecise. Both of these polymorphisms are associated with an odds ratio of ~1.4 and a minor allele frequency of ~5 percent in controls.

The CACNA1C data is based on more common polymorphisms (~30 percent in controls) and an OR~1.2. Again two SNPs are featured (rs1006737 at 7x10e-8, genotyped, and rs1024582 at 2x10-7, imputed). A third region near an uncharacterized gene (on 15q14) is also featured.

Examination of available published data from STEP-UCL and WTCCC on ANK3 and CACNA1C does not show obvious evidence of association among SNPs across each of the named genes, but reasonably consistent signals of modest significance, which is what one might expect, and this does suggest that the featured SNPs are not completely anomalous, but may represent a pattern of genotype deviation across the two genes.

Needless to say, our investigators in the GAIN (Genetic Analysis Information Network) bipolar group are extremely interested in this report and are avidly following the lead provided by Ferreira to attempt to confirm these signals in our own data. I am also pleased to say that GAIN has provided the stimulus for an international consortium that includes representatives from the Ferreira group as well as many other investigators, dedicated to assembling yet larger samples of bipolar cases and controls to elucidate the genetics of this condition through genomewide methods.

This is an important report, and it may represent a breakthrough in bipolar genetic studies. The signals for ANK3 and CACNA1C appear very promising, and we hope that they prove to be consistently observed in other data sets as well. We anticipate that additional confirmed single genes will emerge soon as well, and that the genetic structure of these disorders will be elucidated using similar methods in large data sets in the coming years.

Are we there yet? Have we in the field of bipolar genetics finally been delivered to the promised land by GWAS? For the past year or so since GWAS burst on the scene, we have had to watch with envy as an impressive list of genes were convincingly implicated in a range of other complex diseases like type 2 diabetes, the apparent poster child for GWAS. Now, is it our turn?

The first attempts at individual-level GWAS of bipolar disorder by WTCCC and STEP-UCL were exciting because of their novelty, but the results were not particularly overwhelming. None of the findings withstood correction for the massive multiple testing inherent in GWAS, and those at the top were of ambiguous relevance to bipolar disorder. Confronted with such uninspiring findings, one could not be faulted for experiencing pangs of doubt that maybe for psychiatric disorders, GWAS would prove no better than its dusty old predecessor, the genomewide linkage study, in illuminating the underlying genetic architecture.

Nevertheless, encouraged by the lessons learned from GWAS of type 2 diabetes that the road to the promised land is not paved in individual glory but in collaborations and consortiums, the investigators of WTCCC and STEP-UCL combined samples with a third previously unstudied collection (dubbed ED-DUB-STEP2) to assemble one of the largest samples in bipolar disorder yet to be analyzed by GWAS. The combined sample included 4,387 cases and 6,209 controls genotyped at 325,690 overlapping SNPs, which after imputation yielded data on 1.8 million variants. The results from this effort were recently reported in a manuscript by Ferreira and colleagues published in the latest edition of Nature Genetics.

Despite potential concerns about the genetic and/or clinical heterogeneity of the combined sample (e.g., the genomic inflation factor was estimated to be 1.11, even after controlling for two quantitative indices of population ancestry, which might suggest residual stratification or other unaccounted biases), the results from this effort are encouraging and provide some hope to those who may have been losing their faith. The most notable findings were in ANK3 on chromosome 10q21 and CACNA1C on chromosome 12p13. Multiple SNPs were associated across a 195-kb region of ANK3, and the top SNPs had p-values <5 x 10-8 which is often invoked as an appropriate threshold for genomewide significance in GWAS. Multiple nearby SNPs were also reassuringly associated in CACNA1C, although the top SNP just missed the threshold for genomewide significance. In both ANK3 and CACNA1C the top SNPs were consistently associated in the same direction across all three individual samples, lending credence to the claim that these associations are real. Lending further credence is the fact that ANK3 and CACNA1C are biologically plausible candidates for bipolar disorder, and indeed highlight the possibility that this disorder is an ion channelopathy. Interestingly, the associations with ANK3 and CACNA1C in each of the individual samples were relatively modest and became remarkable only in the combined sample, thus providing support for the rationale to combine samples in order to increase the power to detect those more modest signals that are presumably real but buried amidst the noise of a single study.

Although the evidence is promising, more samples will be needed to confirm the findings before we can say with confidence that we have in hand our first real bipolar susceptibility genes. Fortunately, several such samples have been or will soon be GWASed, including one from GAIN, and it will be of great interest to see whether the current findings are sustained in these new samples. Moreover, there are plans to combine all existing GWAS of bipolar disorder, as part of the initiative referred to as the Psychiatric GWAS Consortium, which should provide an even more definitive picture of the role of ANK3 and CACNA1C, as well as reveal other genes with more modest relative risks whose identities up until now have been obscured.

So, are we there yet? Maybe not just yet, but we are headed in the right direction and I think I can see the promised land.

This paper is an important addition to the psychiatric genetics literature. One important message of it is that increasing the GWAS sample size in a complex neuropsychiatric phenotype such as schizophrenia identifies more common risk loci.

In the first-wave schizophrenia mega-analysis two years ago (Schizophrenia Psychiatric Genome-Wide Association Study Consortium, 2011), five risk loci at genomewide significance were detected, and it was speculated that more would follow with larger sample sizes. In fact, by performing a meta-analysis of a new Swedish schizophrenia sample (about 5,000 patients and 6,000 controls) and the first-wave PGC sample (about 9,000 patients and 12,000 controls) plus follow-up/replication in large, independent samples, the authors now find 22 genomic loci at genomewide significance. This is another important step forward in schizophrenia genetics.

It is reassuring (and important for the scientific community to know) that there is support for some of the previously reported loci. As expected, the findings are getting much more consistent now with the increasing power of the samples. Interestingly, some loci pop up now at genomewide significance that were known from bipolar disorder or combined phenotype analyses (schizophrenia + bipolar), such as CACNA1C and CACNB2. Together with the finding that there is an enrichment of smaller p values in genes encoding calcium channel subunits, there is now growing evidence that calcium signaling is involved in both bipolar disorder and schizophrenia. Calcium signaling is a crucial neuronal process and relevant in a number of human diseases, as the authors nicely review. Importantly, calcium channel complexes may be useful for clinical translation (e.g., as drug targets).

Variation in another gene that was implicated in bipolar disorder before, NCAN, was found at genomewide significance in schizophrenia in the study by Ripke et al. (in fact, an association with schizophrenia was already reported earlier by Mühleisen et al., 2012). It seems that another "theme" of disease-relevant genes may be neurodevelopmental effects in both bipolar disorder and schizophrenia. The different lincRNAs implicated now among the 22 genomewide significant findings may also point in this direction.

What I find particularly interesting are the considerations regarding the much debated genetic architecture of schizophrenia. The authors’ simulations, although certainly still not perfectly exact, substantiate previous calculations that common SNPs make substantial contributions to the risk for schizophrenia.

To my knowledge, for the first time there are estimates regarding the absolute number of common SNPs that contribute to the etiology of schizophrenia: The authors estimate "6,300 to 10,200 independent and mostly common SNPs." While it is difficult to deduce the number of genes or functional units covered by these SNPs, several thousand are well possible. Many of these loci/genes will fall into the same biological pathways, and the identification of a subset of these will probably suffice to identify the most important biological processes. The authors estimate that the top 2,000 loci might be sufficient, and maybe it is even fewer. At the same time, they estimate that such a number of identified loci is not completely out of reach. Sixty thousand patients and 60,000 controls should have enough statistical power to identify between 400 and 1,100 common loci at genomewide significance. Psychiatrists will agree that a great collaborative effort will be required to recruit such a large number of patients. But it is not impossible. The next wave of the PGC schizophrenia group is already moving in this direction.

The recent genomewide association study (GWAS) by Ripke and co-workers is a very important contribution to our knowledge of the genetic underpinnings of schizophrenia. By adding ~5,000 schizophrenia cases and ~6,000 healthy controls from Sweden to the Psychiatric Genomics Consortium (PGC) results from 2011 (PGC, 2011), the authors identified 22 gene loci (including 13 novel) at genomewide significance. These findings confirm that previous schizophrenia GWAS have been statistically underpowered, and that increasing the sample size is a successful approach to bridge the gap between the high heritability estimates in schizophrenia and low variance explained by currently identified genetic variants.

With increasing sample size, consistency is also enhanced. Reassuringly, of the 100 most significant SNPs in the Sweden/PGC meta-analysis, 90 percent had the same sign. Moreover, previously reported signals from immune-related genes in the MHC region on chromosome 6 were confirmed, as well as genes encoding calcium channel subunits, miR-137, and targets of miR-137. Additionally, the authors found enrichment in an extended set of genes with predicted miR-137 target sites. The results also pointed to long intergenic noncoding RNAs (lincRNAs), as 13 out of 22 identified regions contain lincRNAs. Interestingly, there is evidence that lincRNAs have functions related to epigenetic regulation.

Using two newly developed methods—genomewide complex trait analysis (GCTA) and applied Bayesian polygenic analysis (ABPA)—the authors estimated that common variants account for 52 percent and 78 percent, respectively, of the phenotypic variation in schizophrenia. These numbers are, although imprecisely, in accordance with the high heritability estimates from meta-analyses on twin studies (81 percent) (Sullivan et al., 2003) and large epidemiological studies (64 percent) (Lichtenstein et al., 2009), and indicate that a substantial proportion of the genetic risk for schizophrenia can be explained by common variants identified in GWAS. With the ABPA method, the authors also estimated that between 6,300 and 10,200 SNPs explain 50 percent of the variance in schizophrenia susceptibility. This clearly shows the high polygenicity in schizophrenia.

The authors suggest that 60,000 schizophrenia cases and 60,000 controls are warranted to identify ~800 markers at genomewide significance level. With the combined effort from the PGC, this might be achievable, and a larger proportion of the hidden heritability will undoubtedly be revealed. However, when using the PGC schizophrenia sample as the discovery set and the Swedish sample as the test set, explained variance (Nagelkerke pseudo R2) was only ~0.06 (contrasted by an impressive significance level of 2 x 10-114). This is slightly disappointing, as the explained variance with similar methodology was found to be ~0.03 in a study by Purcell and co-workers in 2009 (Purcell et al., 2009). By increasing the sample size five times (~6,000 vs. ~30,000 individuals), explained variance has only increased from 3 to 6 percent. Although a further increase in the PGC sample will provide important information on risk variants, a refinement of the method is probably also needed to discover larger parts of the hidden heritability for the highly polygenic disorder schizophrenia. Such methods might include Bayesian models for weighing SNPs based on prior evidence, for example, related to pleiotropic effects (Andreassen et al., 2013) and genic annotation (Schork et al., 2013). A combination of large numbers and methodological refinement might prove particularly potent, both in terms of explaining more of the variance and identifying underlying molecular pathways.

I think these studies do represent real progress. Finding genetic support for particular pathways provides unique evidence for a causative role of these pathways in disease. Why didn't the case-control study point to individual genes? Disorders such as schizophrenia may be more like a plane crash than a typical inherited disease: Since many things can go wrong, each crash is different, but damage to key systems is very likely to lead to a bad outcome. The finding in Fromer et al. that there are 18 genes with recurrent deleterious de novo events should allow scientists to focus on these genes as especially important. The overlaps with autism and intellectual disability are interesting, though not entirely unexpected. Will we also see gene overlaps with illnesses such as bipolar disorder? It wouldn't surprise me if some of the same genes are involved, but with fewer, less deleterious hits.